Abstract: A system for developing and implementing empirically derived algorithms to generate decision rules to predict invalidity of subject reported data and fraud with research protocols in surveys allows for the identification of complex patterns of variables that detect or predict subject invalidity of subject reported data and fraud with the research protocol in the survey. The present invention may also be used to monitor invalidity of subject reported data within a research protocol to determine preferred actions to be performed. Optionally, the invention may provide a spectrum of invalidity, from minor invalidity needing only corrective feedback, to significant invalidity requiring subject removal from the survey. The algorithms and decision rules can also be domain-specific, such as detecting invalidity or fraud among subjects in a workplace satisfaction survey, or demographically specific, such as taking into account gender or age.

Abstract: A system for developing and producing clinical endpoints based on patient reported outcome data utilizing algorithms to generate decision rules to evaluate patient reported outcome type questionnaire is provided. The questionnaires can include health-related quality of life questionnaires and can predict the reliability of endpoints in supporting one or more medical labeling claims.

Abstract: A system for developing and implementing empirically derived algorithms to generate decision rules to determine participant noncompliance and fraud with research protocols in clinical trials allows for the identification of complex patterns of variables that detect or predict participant noncompliance and fraud with research protocol, including performance and enrollment goals, in the clinical trial. The data may be used to overall predict the performance of any participant in a clinical trial, allowing selection of participants that tend to produce useful, high-quality results. The present invention can also be used to monitor participant compliance with the research protocol and goals to determine preferred actions to be performed. Optionally, the invention may provide a spectrum of noncompliance, from minor noncompliance needing only corrective feedback, to significant noncompliance requiring participant removal from the clinical trial or from future clinical trials.

Abstract: A system for developing and implementing empirically derived algorithms to generate decision rules to predict invalidity of subject reported data and fraud with research protocols in surveys allows for the identification of complex patterns of variables that detect or predict subject invalidity of subject reported data and fraud with the research protocol in the survey. The present invention may also be used to monitor invalidity of subject reported data within a research protocol to determine preferred actions to be performed. Optionally, the invention may provide a spectrum of invalidity, from minor invalidity needing only corrective feedback, to significant invalidity requiring subject removal from the survey. The algorithms and decision rules can also be domain-specific, such as detecting invalidity or fraud among subjects in a workplace satisfaction survey, or demographically specific, such as taking into account gender or age.

Abstract: A system for developing and implementing empirically derived algorithms to generate decision rules to determine participant noncompliance and fraud with research protocols in clinical trials allows for the identification of complex patterns of variables that detect or predict participant noncompliance and fraud with research protocol, including performance and enrollment goals, in the clinical trial. The data may be used to overall predict the performance of any participant in a clinical trial, allowing selection of participants that tend to produce useful, high-quality results. The present invention can also be used to monitor participant compliance with the research protocol and goals to determine preferred actions to be performed. Optionally, the invention may provide a spectrum of noncompliance, from minor noncompliance needing only corrective feedback, to significant noncompliance requiring participant removal from the clinical trial or from future clinical trials.

Abstract: A system for developing and implementing empirically derived algorithms to generate decision rules to predict subject noncompliance and fraud with research protocols in clinical trials allows for the identification of complex patterns of variables that detect or predict subject noncompliance and fraud with research protocol in the clinical trial. The present invention can also be used to monitor subject compliance with the research protocol to determine preferred actions to be performed. Optionally, the invention may provide a spectrum of noncompliance, from minor noncompliance needing only corrective feedback, to significant noncompliance requiring subject removal from the clinical trial.

Abstract: The present invention is designed to develop research protocols for clinical trials. The invention also can track and enhance subject compliance with a research protocol. The invention further provides evaluability data related to subject performance in the clinical trial. According to an alternative embodiment of the invention, a portable electronic device is used to query and collect data from the subject.

Abstract: A system for developing and implementing empirically derived algorithms to generate decision rules to predict subject noncompliance and fraud with research protocols in clinical trials allows for the identification of complex patterns of variables that detect or predict subject noncompliance and fraud with research protocol in the clinical trial. The present invention can also be used to monitor subject compliance with the research protocol to determine preferred actions to be performed. Optionally, the invention may provide a spectrum of noncompliance, from minor noncompliance needing only corrective feedback, to significant noncompliance requiring subject removal from the clinical trial.